Obviously I haven’t watched all 75 different videos, but I did watch a few so I’ve included some of the things I learned, announcements on new features, links to some of the especially interesting sessions, and my notes from a couple of those sessions below.

The Timeline Storyteller Visualization is Coming to Power BI this Summer!

I forgot to mention this in my day 1 recap so I figured I’d mention it here, but if you watched the MS Data Summit keynote, you definitely saw the Timeline Storyteller Power BI custom visualization because it was extremely impressive.

What’s New and What’s Coming in Power BI Desktop?

This session was all about what’s new to Power BI Desktop and what kind of features and capabilities are coming down the pipes in the near future. This session was presented by Kim Manis, Amanda Cofsky, and Will Thompson, all members of the Power BI product team.

First the team gave an overview of all the new features in connectivity, modeling, report authoring, and custom visualizations in Power BI Desktop that have arrived in the past 6 months, but we already know about all of that stuff.

The really interesting part of this session was the team’s discussion on the features that will be coming to Power BI Desktop in the near future. This was the whole reason I watched this session. There were four main areas that the team discussed related to new features coming to Power BI Desktop so I’ll highlight that and add some details:

1. Enterprise readiness

How do you roll out Power BI Desktop across a large organization? Because Power BI Desktop is updated monthly, this is tough for big orgs. So the team is looking out rolling out Power BI Desktop via the Windows Store so you’ll get automatic updates to Power BI Desktop in the background! That would be very nice. They’re also considering using Windows Update to roll this out, but this would be more involved. These are just things the team is thinking about and not set in stone.

2. Enhanced connectivity

The team has already added over 70 data sources and more are coming. With the custom data connectors, that number is even large. The team also stated that they’re investing in how to improve working with slower and larger data sets. The team didn’t offer any big details about this area, but something to be aware of.

3. Visual and report canvas completeness

The team will be continuing to iterate on the customization and formatting of the native visualizations. For instance, the team is continuing to add formatting option, like font settings, axis settings, and more. There’s still more work to be done here and the team is on top of it.

They’re also going to be working on improving the theme capabilities. They’re planning on, not only allowing color themes, but also allowing “style themes”, which would include colors, fonts, grid lines, etc., that can be applied to any chart. So for instance, you could define a style theme and somehow import that into your Power BI Desktop files. I know personally that this would be extremely useful for many of my customers.

4. Exploration and discovery

There’s a lot of work being done here, some of what we know. We saw some of those things during day 1, such as drill through and quick insights.

Kim provided more details on how quick insights bring the power of machine learning to Power BI Desktop. Kim right-clicked a point on the line chart which exposed the Analyze option and then the options to Explain the decrease and Find additional insights. Notice that the point Kim right-clicked on is a low point in the line chart. Power BI Desktop automatically detected the dip and offered the option to “Explain the decrease”. Very cool.

When Kim selected Explain the decrease, the following waterfall chart was displayed. Kim then clicked the + button in the top right to add the chart to the report. I think this is an excellent feature that would allow me to quickly add useful visualizations with useful insights to a report that we might have missed.

Kim also showed some other useful insights that can be added to the report.

This appears to be very similar to the Quick Insights feature that we currently have available in the Power BI server but sharpened a bit and implemented in Power BI Desktop. The Quick Insights visualizations in Power BI Desktop can also be edited and customized by the report designer.

One really neat thing here was that Kim used ctrl+click to select two bars on a bar chart. Then she right-clicked, selected Analyze, and then select What’s different?

This appear to automatically generate a visual comparison of the two data elements, in this case Samuel L. Jackson vs. Stan Lee. I think this is very cool!

Kim was clear that this is just the start of development on this feature. They’re going to start with a couple gestures to begin with but then continue to expand this feature and add new capabilities. I’m excited about this one and how it can make analysis faster and easier.

Think Like a Freak Closing Keynote with Stephen Dubner and Steven Levitt

For the closing keynote, we heard from Stephen Dubner and Steven Levitt, the authors of Freakonomics (twitter). This was a super interesting closing keynote, so you should definitely go watch it (or at least listen to it as you work like I did) when you have about an hour. Interestingly enough, Dubner began the talk discussing turkey sex (or lack thereof). No, that’s not a typo. You can watch the closing keynote recording to hear Dubner discuss why turkeys don’t have sex and have to be artificially inseminated (never thought I’d be writing these words). But the reason this is interesting to the Freakonomics guys is that it represents exactly what they do:

Using data to discover incentives that change behavior

Then Levitt took the stage and gave some advice related to using data to change cultures.

1. Its really important, if you’re in the data business, to figure out how to dramatically reduce the marginal cost of producing data analysis. Levitt discussed how the time and cost of doing data analysis should be reduced in order to ease the data analysis process. And what Levitt was referring to was the processing of the data, structure of the data, time to access the data, and all that goes into data analysis. How can we make data analysis faster and easier? Just a side note here: This is what a data warehouse is important. Often the data structures used to support the execution of the business are not optimal for supporting the analysis of the business. I actually did a webinar at Pragmatic Works on this topic, which you can watch here.

The takeaway for me here was that if you can’t ease the data analysis process that means that the cost to use your data in a useful way potentially remains expensive and time intensive. We want to reduce the marginal costs of data analysis.

2. The simpler the better, referring to data analysis and data visualizations. When you’re working with people who don’t know much about data, simpler is going to be better and make more sense. The example Levitt gave, was a simple bar chart with one big bar and one little bar. You don’t have to be a data genius to understand a simple bar chart and that was the point Levitt was making.

3. They never interpret data. We simply show the data. If we could just show people the data in a simple format, they’ll be able to interpret it themselves and make their own business decisions.

4. Always bring data to the meeting. Every corporate meeting involves people talking about a problem and then people talking about, “Well I think the problem is this or that and this is what we should do about it”. But the people talking about what they think the problem is and what should be done weren’t bringing data to the meetings or reviewing the analytics. Levitt said that they started bringing data to every meeting. Then they’d actually show the data and then derive the solution from the data. Pretty insightful stuff here.

5. Simply whenever possible let everyone else take the credit. If you’re the data guy, then you know that you’re the one responsible for the solution. And everyone else knows it, too. But let the other people, whether it’s the executive, the manager, or the team lead, take the credit. Part of corporate culture is that everyone is afraid to said “I don’t know”. If you let others take the credit then they walk away happy with the analysis you’ve done.

6. If you work in an organization that, even after you’ve done all these other things with data, still does not value your time, skills, effort, and all you offer, then you quit. Levitt’s point is that there is no benefit to wasting your time in an organization with people that do not value data. There’s a shortage of people with data skills and the evidence suggest, based on Levitt’s take, that you’ll be happier.

7. When you want to convey the meaning of the data, tell a story. First, we’re all narcissist, according to Dubner, and we put ourselves in the place of the person in the story. Stories appeal to the narcissist in all of. Also, stories make the numbers taste better. So stories are incredibly important when you’re trying to sell someone on a theory. A story illustrates a theory.

This closing keynote was really excellent and I’d highly recommend that you watch it completely.